





'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_   :       int       =     logging.get_logger(__name__)

lowerCAmelCase_   :       Optional[Any]       =     {
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}
class 							__SCREAMING_SNAKE_CASE    (lowerCamelCase_ ):






				"""simple docstring"""







				__a  ='markuplm'




				def __init__(    self	:   Dict   ,							__a	:   Optional[int]=3_05_22   ,							__a	:   str=7_68   ,							__a	:   List[Any]=12   ,							__a	:   Dict=12   ,							__a	:   Tuple=30_72   ,							__a	:   str="gelu"   ,							__a	:   Optional[Any]=0.1   ,							__a	:   Optional[int]=0.1   ,							__a	:   int=5_12   ,							__a	:   Optional[int]=2   ,							__a	:   str=0.02   ,							__a	:   int=1e-1_2   ,							__a	:   int=0   ,							__a	:   Dict=0   ,							__a	:   List[str]=2   ,							__a	:   Any=2_56   ,							__a	:   Any=10_24   ,							__a	:   int=2_16   ,							__a	:   str=10_01   ,							__a	:   Optional[Any]=32   ,							__a	:   Tuple=50   ,							__a	:   str="absolute"   ,							__a	:   Union[str, Any]=True   ,							__a	:   str=None   ,							**__a	:   Tuple   ,							):
								super().__init__(
								    pad_token_id=__a   ,							bos_token_id=__a   ,							eos_token_id=__a   ,							**__a   ,							)
								_a				      =					vocab_size
								_a				      =					hidden_size
								_a				      =					num_hidden_layers
								_a				      =					num_attention_heads
								_a				      =					hidden_act
								_a				      =					intermediate_size
								_a				      =					hidden_dropout_prob
								_a				      =					attention_probs_dropout_prob
								_a				      =					max_position_embeddings
								_a				      =					type_vocab_size
								_a				      =					initializer_range
								_a				      =					layer_norm_eps
								_a				      =					position_embedding_type
								_a				      =					use_cache
								_a				      =					classifier_dropout
								# additional properties
								_a				      =					max_depth
								_a				      =					max_xpath_tag_unit_embeddings
								_a				      =					max_xpath_subs_unit_embeddings
								_a				      =					tag_pad_id
								_a				      =					subs_pad_id
								_a				      =					xpath_unit_hidden_size




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


